LEADER 01948oam 2200457 450 001 9910811547303321 005 20190911103511.0 010 $a1-4698-4228-9 010 $a1-4511-7830-1 010 $a1-4698-7766-X 035 $a(OCoLC)873761812 035 $a(MiFhGG)GVRL8SYD 035 $a(EXLCZ)993460000000122715 100 $a20141220h20132013 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aAtlas of EEG patterns 205 $aSecond edition. 210 1$aPhiladelphia :$cWolters Kluwer Health / Lippincott Williams & Wilkins,$d[2013] 210 4$d?2013 215 $a1 online resource (ix, 457 pages) $cillustrations 225 0 $aGale eBooks 300 $aDescription based upon print version of record. 311 $a1-4511-0963-6 320 $aIncludes bibliographical references and index. 327 $asection I. Introduction -- section II. Categorization -- section III. Patterns. 330 $aAtlas of EEG Patterns, Second Edition The electroencephalogram (EEG) is essential to the accurate diagnosis of many neurologic disorders. The Second Edition of Atlas of EEG Patterns sharpens readers' interpretation skills with an even larger array of both normal and abnormal EEG pattern figures and text designed to optimize recognition of telltale findings. Trainees will benefit from hundreds of EEG figures, helping them spot abnormalities and identify the pattern name. Experienced neurologists will find the book excellent as a quick reference and when trying to distinguish a finding from simi 606 $aElectroencephalography$vAtlases 615 0$aElectroencephalography 676 $a467 700 $aStern$b John M$01055571 702 $aEngel$b Jerome$cJr., 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910811547303321 996 $aAtlas of EEG patterns$94012648 997 $aUNINA LEADER 01749nam 2200397z- 450 001 9910346767303321 005 20210211 010 $a1000060221 035 $a(CKB)4920000000100850 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/54603 035 $a(oapen)doab54603 035 $a(EXLCZ)994920000000100850 100 $a20202102d2017 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aNew Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty 210 $cKIT Scientific Publishing$d2017 215 $a1 online resource (XII, 243 p. p.) 311 08$a3-7315-0590-8 330 $aMultidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images. 610 $a3D Bildanalyse 610 $aAlgorithmen 610 $aAlgorithms 610 $aData Mining 610 $aDevelopmental Biology 610 $aEntwicklungsbiologie 610 $aSoftware 610 $aSoftware3D Image Analysis 700 $aStegmaier$b Johannes$4auth$01323753 906 $aBOOK 912 $a9910346767303321 996 $aNew Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty$93035813 997 $aUNINA